The writing is on the LED screen: our jobs are ours for as long as the robots don't want them. But no one could have predicted they'd come for the gallery guides first. The world's first humanoid robot has started taking tours at the Art Gallery of Western Australia (AGWA) – two a month, for now. It seems a cruel twist of fate to have art history graduates among the first to be made redundant by robots; they have a hard enough time finding work as it is. But when I travel to AGWA to meet the future of work, it is plugged into the wall, charging.

Advanced digital technologies are swiftly changing the kinds of skills that jobs require. How are improvements in digital technology changing the nature of human work? A team of researchers from the MIT Sloan School of Management and the Masdar Institute in Masdar City, Abu Dhabi, have taken a close look at that question. In a paper called "Racing With and Against the Machine: Changes in Occupational Skill Composition in an Era of Rapid Technological Advance," Frank MacCrory, George Westerman and Erik Brynjolfsson of the MIT Sloan School and Yousef Alhammadi of the Masdar Institute studied the U.S. government's O*NET database of occupational skill requirements in 2006 and 2014 and analyzed the types of skills that jobs required in both years. Their paper was named the best conference paper at the 2014 International Conference on Information Systems.

Just mention artificial intelligence or AI and the imaginative mind conjures up images of Terminator machines taking over the world. Thankfully, today's picture is much more positive. Businesses are beginning to harness the power of AI and robotics to grow profit. Many use the technology to collaborate with their staff. Artificial intelligence, meaning the ability of computers to make judgments, suddenly appeared real in the 1990s when computers began to beat chess world champions. Its sophistication has improved dramatically and this year Google's DeepMind won the immensely complicated Go board game.

The first wave of robots taking over human jobs is upon us. Apple Inc. AAPL, 0.79% supplier Foxconn Technology Co. 2354, 0.14% has replaced 60,000 human workers with robots in a single factory, according to a report in the South China Morning Post, initially published over the weekend. This is part of a massive reduction in headcount across the entire Kunshan region in China's Jiangsu province, in which many Taiwanese manufacturers base their Chinese operations. In a statement to MarketWatch, Foxconn Technology Group confirmed that it has been automating its manufacturing facilities throughout China, including Kunshan, for "many years," which it says has freed up its employees to focus on higher value-added elements of the manufacturing process, such as research and development, process control and quality control. "Across all of our facilities today, we are applying robotics engineering and other innovative manufacturing technologies to replace repetitive tasks previously done by employees," Foxconn said.

What are the fundamental limitations inherent in machine learning systems? That's the central question of a potential new DARPA program known as the Fundamental Limits of Learning (Fun LoL) which according to the researchers will address how the quest for the ultimate learning machine can be measured and tracked in a systematic and principled way. "It's not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren't easily generalized to solve related problems or that can't be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently--but how much so? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined," DARPA stated.

In the quest for the most capable robotic bug (which is a quest that many roboticists seem to be on, because robotic bugs are nifty), some of the most exciting designs are inspired by the dynamic, multi-modal ways in which insects are conquering the world. Combining skills like running with skills like jumping can make little robots much more efficient movers, allowing them to go farther on a charge as well as helping them surmount obstacles and rough terrain. Most of the small jumping robots we've seen before use a spring mechanism with a latch on it. The latch makes the spring state binary: the spring gets all wound up, the latch holds it, and then disengages on command, releasing all of the energy in the spring in one go. You can get a lot of power this way, but it's an all or nothing sort of thing, so the magnitude (height, distance, whatever) isn't controllable.

By this point, frequent Uber users are probably familiar with Pool, the company's pseudo-carpooling service. UberPool works by finding riders who are heading along similar routes and grouping their trips together. A driver might pick up you, and then another passenger, and maybe even a third, before dropping you off at your destination. For this inconvenience and added human interaction, Uber promises Pool riders fixed fares, often at steep discounts to what it charges for private cars. But there's an even more important detail about Pool that's often overlooked: It doesn't show riders surge pricing.

China's artificial intelligence industry received a huge boost of validation from the government on Wednesday, which announced its plans to create a "100 billion level" ( 15 billion USD) artificial intelligence market by 2018. According to state-owned media Xinhua News Agency, the government plans to roll out projects in smart home applications, smart cars, unmanned systems, wearables, and robotics over the next three years. "According to the plan, China will improve the country's economy and society, disrupt the core technologies of artificial intelligence, and increase our smart hardware supply capabilities," stated the government in its announcement. "Over the next three years, the country will build a solid foundation for an innovative, active, collaborative, eco-friendly, and safe artificial intelligence industry." As per usual, the government's announcement was vague.

Jerry Kaplan: will discuss his his book, Humans Need Not Apply, at the Bay Area Book Festival on Sat. This article is brought to you by the Bay Area Book Festival. Fifty years ago, we could not have imagined phones that communicated without wires. Ten years ago, driverless cars were something out of a science-fiction novel -- not on assembly lines. Artificial intelligence has already arrived -- hello, Siri and GPS -- but has only just begun the trajectory that promises to change our personal and work lives radically.

We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than boosted trees, random forests and logistic regression when unlabeled examples are available.